Cancer cell-derived IL-1β reverses chemo-immunotherapy resistance in non-small cell lung cancer
Bibliographic record
Abstract
Many non-small cell lung cancer (NSCLC) patients remain unresponsive to the current standard of care, which includes chemotherapy and immune checkpoint inhibitors, like anti-PD-1/PD-L1 antibodies. While interleukin (IL)-1β is known to promote lung cancer growth in humans and mice, we show here that IL-1β administration or overexpression overcomes resistance to classical chemo-immunotherapy (cisplatin/pemetrexed/anti-PD-1) in mouse lung cancer models. The antitumor effects of IL-1β rely on cancer cell-derived CXCL10 which mediates CD8 T cell recruitment at the tumor site. In lung cancer cells, Thioredoxin Interacting Protein (TXNIP) induces mitochondrial DNA (mtDNA) release in the cytosol, activating Absence in Melanoma 2 (AIM2) inflammasome, which subsequently triggers IL-1β and CXCL10 secretion, thereby reversing chemo-immunotherapy resistance. The clinical relevance of our findings is supported by the transcriptomic analysis of patient tumors, showing that high expression of IL1B, IL1R1, AIM2 and/or TXNIP is associated with better response to immunotherapy in NSCLC patients. Additionally, drug screening identifies MEK and MDM2 inhibitors as inducers of TXNIP expression capable of reversing resistance to chemo-immunotherapy. This study highlights a positive role of IL-1β in lung cancer treatment and suggests that enhancing IL-1β production at the tumor site can overcome resistance to chemo-immunotherapy. Interleukin (IL)-1β has been shown to promote tumour growth in non-small cell lung cancer (NSCLC). Here the authors show using chemo-immunotherapy resistant mouse tumour models that IL-1β improves CD8 T cell recruitment in a CXCL10-dependent manner and IL-1β therapy could be a useful adjunct to chemotherapy and anti-PD-1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".